标题： 基于人工智能的细胞形态学图像合成用于医学诊断 显示英文标题

标题： AI-Driven Cytomorphology Image Synthesis for Medical Diagnostics

摘要： 生物医学数据集通常包含大量样本不平衡，并受到严格的隐私约束，这共同阻碍了准确机器学习模型的发展。 一种潜在的解决方案是生成合成图像，因为这可以在保持患者隐私的同时提高数据可用性。 然而，生成足够高质量的合成图像以训练稳健分类器仍然具有挑战性。 在本工作中，我们专注于单个白细胞的分类，这是诊断血液系统疾病（如急性髓系白瘤（AML），一种严重的血液癌症）的关键组成部分。 我们展示了如何通过使用LoRA权重并借助目标白细胞类别的真实少量样本引导的微调稳定扩散模型生成的合成图像，可以增强有限数据的分类器性能。 在训练ResNet分类器时，通过向一个小且高度不平衡的真实数据集每类添加5000张合成图像，准确率从27.3%提高到78.4%（+51.1%）。 对于基于CLIP的分类器，准确率从61.8%提高到76.8%（+15.0%）。 合成图像与真实图像非常相似，它们可以帮助克服数据集的限制，提高模型的泛化能力。 我们的结果确立了合成图像作为生物医学研究中的工具，提高了机器学习模型，并促进了医学诊断和研究。 显示英文摘要

摘要： Biomedical datasets often contain a large sample imbalance and are subject to strict privacy constraints, which together hinder the development of accurate machine learning models. One potential solution is to generate synthetic images, as this can improve data availability while preserving patient privacy. However, it remains difficult to generate synthetic images of sufficient quality for training robust classifiers. In this work, we focus on the classification of single white blood cells, a key component in the diagnosis of hematological diseases such as acute myeloid leukemia (AML), a severe blood cancer. We demonstrate how synthetic images generated with a fine-tuned stable diffusion model using LoRA weights when guided by real few-shot samples of the target white blood cell classes, can enhance classifier performance for limited data. When training a ResNet classifier, accuracy increased from 27.3\% to 78.4\% (+51.1\%) by adding 5000 synthetic images per class to a small and highly imbalanced real dataset. For a CLIP-based classifier, the accuracy improved from 61.8\% to 76.8\% (+15.0\%). The synthetic images are highly similar to real images, and they can help overcome dataset limitations, enhancing model generalization. Our results establish synthetic images as a tool in biomedical research, improving machine learning models, and facilitating medical diagnosis and research.